Efficient Sample Location Selection for Query Zone in Geo-Social Networks

While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency DAIM heavily relies on sample selection. Specifically, online seeding performance sensitive to promoted nearest location, offline precomputation number locations. However, there no work fully study selection for networks. To do this, we first formalize under reasonable assumption that always adheres distribution (query zone). Then, propose two efficient sampling approaches based facility analysis, which one most well-studied areas operations research, these are denoted by Facility Location Sampling (FLS) Conditional Based (CFLS), respectively. FLS conducts one-time selection, CFLS extends continuous process, so an advertising service can be started immediately without lot Our experimental results real datasets demonstrate effectiveness proposed methods. both achieve better than existing methods problem, initialize matter seconds objective after large